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type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022\/\/cancerpreventionresearch.aacrjournals.org\/sites\/default\/files\/advagg_css\/css__KwIbTmK5u_TU0NIDnj2cXC8qhcT3ubSigju5zweyvGo__pCcdir97KnqB2daNuh0MLpBlfROgYmoCj9xpkfJGITs__KYukzxw2SeyOT2YqVDA-Rl6BtY9KNxmhEHJBEnvKs0Q.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink rel=\u0027stylesheet\u0027 type=\u0027text\/css\u0027 href=\u0027\/sites\/all\/modules\/contrib\/panels\/plugins\/layouts\/onecol\/onecol.css\u0027 \/\u003E\u003C\/head\u003E\u003Cbody\u003E\u003Cdiv class=\u0022panels-ajax-tab-panel panels-ajax-tab-panel-jnl-aacr-tab-data\u0022\u003E\u003Cdiv class=\u0022panel-display panel-1col clearfix\u0022 \u003E\n \u003Cdiv class=\u0022panel-panel panel-col\u0022\u003E\n \u003Cdiv\u003E\u003Cdiv class=\u0022panel-pane pane-article-fig-data\u0022 \u003E\n \n \u003Ch2 class=\u0022pane-title\u0022\u003EArticle Figures \u0026amp; Data\u003C\/h2\u003E\n \n \n \u003Cdiv class=\u0022pane-content\u0022\u003E\n \u003Cdiv class=\u0022elements-frag-data highwire-markup\u0022 id=\u0022fig-data\u0022\u003E\u003Cdiv id=\u0022fig-data-figures\u0022 class=\u0022group frag-figure\u0022\u003E\u003Cdiv class=\u0022fig-data-title-jump clearfix\u0022\u003E\u003Ch3 class=\u0022fig-data-group-title\u0022\u003EFigures\u003C\/h3\u003E\u003Cdiv class=\u0022fig-data-jump-links\u0022\u003E\u003Cul class=\u0022fig-data-jump-links-list links inline\u0022\u003E\u003Cli class=\u0022table first\u0022\u003E\u003Ca href=\u0022#fig-data-tables\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-table link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-down\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003ETables\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022additional-files last\u0022\u003E\u003Ca href=\u0022#fig-data-additional-files\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-additional-files link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-down\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003EAdditional Files\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022item-list\u0022\u003E\u003Cul class=\u0022fig-data-list clearfix\u0022 id=\u0022fragments-fig\u0022\u003E\u003Cli class=\u0022first\u0022\u003E\u003Cdiv class=\u0022element-fig-data clearfix figure-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022fig-expansion\u0022 id=\u0022F1\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F1.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022Illustration of path and oncogenetic tree mutational models inferred from cross-sectional data, and all possible temporal orders of clonal mutations that are consistent with the models. Each arrow between circles represents the acquisition of a new mutation in models inferred from cross-sectional data, and squares represent the accumulation of a new mutation in a clone during the evolution of a tumor. A, the path model of carcinogenesis implies a linear order of sequential mutations from wild type through A, B, and C, in order. B, these are the temporal mutations that a cell lineage, or clone of cells, could acquire during evolution and still be consistent with the cross-sectional path model in A. All other sequences of mutations are inconsistent with the cross-sectional path model (e.g., B, C, AC, and BAC are inconsistent). C, the oncogenetic tree model of carcinogenesis implies that all tumors begin as wild type and can next acquire either mutation A or B. In addition, C can only occur at any point after mutation A has occurred. D, all temporal mutations acquired by a clone that are consistent with the cross-sectional oncogenetic tree in C. Note that the order A, B, C is consistent because C occurs after A.\u0022 class=\u0022highwire-fragment fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1009679262\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;Illustration of path and oncogenetic tree mutational models inferred from cross-sectional data, and all possible temporal orders of clonal mutations that are consistent with the models. Each arrow between circles represents the acquisition of a new mutation in models inferred from cross-sectional data, and squares represent the accumulation of a new mutation in a clone during the evolution of a tumor. A, the path model of carcinogenesis implies a linear order of sequential mutations from wild type through A, B, and C, in order. B, these are the temporal mutations that a cell lineage, or clone of cells, could acquire during evolution and still be consistent with the cross-sectional path model in A. All other sequences of mutations are inconsistent with the cross-sectional path model (e.g., B, C, AC, and BAC are inconsistent). C, the oncogenetic tree model of carcinogenesis implies that all tumors begin as wild type and can next acquire either mutation A or B. In addition, C can only occur at any point after mutation A has occurred. D, all temporal mutations acquired by a clone that are consistent with the cross-sectional oncogenetic tree in C. Note that the order A, B, C is consistent because C occurs after A.\u0026lt;\/div\u0026gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cspan class=\u0022hw-responsive-img\u0022\u003E\u003Cimg class=\u0022highwire-fragment fragment-image lazyload\u0022 alt=\u0022Figure 1.\u0022 src=\u0022data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\u0022 data-src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F1.medium.gif\u0022 width=\u0022440\u0022 height=\u0022405\u0022\/\u003E\u003Cnoscript\u003E\u003Cimg class=\u0022highwire-fragment fragment-image\u0022 alt=\u0022Figure 1.\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F1.medium.gif\u0022 width=\u0022440\u0022 height=\u0022405\u0022\/\u003E\u003C\/noscript\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F1.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 1.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F1.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/28357\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 1.\u003C\/span\u003E \n\u003Cp id=\u0022p-7\u0022\u003EIllustration of path and oncogenetic tree mutational models inferred from cross-sectional data, and all possible temporal orders of clonal mutations that are consistent with the models. Each arrow between circles represents the acquisition of a new mutation in models inferred from cross-sectional data, and squares represent the accumulation of a new mutation in a clone during the evolution of a tumor. A, the path model of carcinogenesis implies a linear order of sequential mutations from wild type through A, B, and C, in order. B, these are the temporal mutations that a cell lineage, or clone of cells, could acquire during evolution and still be consistent with the cross-sectional path model in A. All other sequences of mutations are inconsistent with the cross-sectional path model (e.g., B, C, AC, and BAC are inconsistent). C, the oncogenetic tree model of carcinogenesis implies that all tumors begin as wild type and can next acquire either mutation A or B. In addition, C can only occur at any point after mutation A has occurred. D, all temporal mutations acquired by a clone that are consistent with the cross-sectional oncogenetic tree in C. Note that the order A, B, C is consistent because C occurs after A.\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv class=\u0022element-fig-data clearfix figure-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022fig-expansion\u0022 id=\u0022F2\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F2.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022Explanation of sampling strategies using a representative simulation. Each simulation represents a single tumor and the order of mutations was inferred from the set of all tumors, using 2 alternative strategies. A, the number of cells in this simulation increased over time until it became large enough to trigger detection of cancer (labeled as \u0026quot;e\u0026quot;). The cross-sectional path model was derived from sampling all tumors on the basis of their size. Most cross-sectional studies take 1 biopsy per patient and categorize the tumors by size (and\/or grade). To simulate this, we took biopsies of each tumor at prespecified sizes (dashed lines) and then assayed the majority genotype for the biopsy (B). Data from each size class were summarized across all simulations to measure the frequency of mutations for each size class (see Fig. 4A). C, the alternative to cross-sectional sampling is to reconstruct the cell lineages for each tumor. This was done using 5 randomly selected cells from the final time point \u0026#x201C;e.\u0026#x201D; During simulations, the cell lineages were recorded, as exact lineage relationships could be derived from detailed genetic data for each cell. The phenotypic effect of each mutation is represented by a different color, defined in Figure 3B.\u0022 class=\u0022highwire-fragment fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1009679262\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;Explanation of sampling strategies using a representative simulation. Each simulation represents a single tumor and the order of mutations was inferred from the set of all tumors, using 2 alternative strategies. A, the number of cells in this simulation increased over time until it became large enough to trigger detection of cancer (labeled as \u0026quot;e\u0026quot;). The cross-sectional path model was derived from sampling all tumors on the basis of their size. Most cross-sectional studies take 1 biopsy per patient and categorize the tumors by size (and\/or grade). To simulate this, we took biopsies of each tumor at prespecified sizes (dashed lines) and then assayed the majority genotype for the biopsy (B). Data from each size class were summarized across all simulations to measure the frequency of mutations for each size class (see Fig. 4A). C, the alternative to cross-sectional sampling is to reconstruct the cell lineages for each tumor. This was done using 5 randomly selected cells from the final time point \u0026#x201C;e.\u0026#x201D; During simulations, the cell lineages were recorded, as exact lineage relationships could be derived from detailed genetic data for each cell. The phenotypic effect of each mutation is represented by a different color, defined in Figure 3B.\u0026lt;\/div\u0026gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cspan class=\u0022hw-responsive-img\u0022\u003E\u003Cimg class=\u0022highwire-fragment fragment-image lazyload\u0022 alt=\u0022Figure 2.\u0022 src=\u0022data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\u0022 data-src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F2.medium.gif\u0022 width=\u0022440\u0022 height=\u0022321\u0022\/\u003E\u003Cnoscript\u003E\u003Cimg class=\u0022highwire-fragment fragment-image\u0022 alt=\u0022Figure 2.\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F2.medium.gif\u0022 width=\u0022440\u0022 height=\u0022321\u0022\/\u003E\u003C\/noscript\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F2.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 2.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F2.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/28389\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 2.\u003C\/span\u003E \n\u003Cp id=\u0022p-13\u0022\u003EExplanation of sampling strategies using a representative simulation. Each simulation represents a single tumor and the order of mutations was inferred from the set of all tumors, using 2 alternative strategies. A, the number of cells in this simulation increased over time until it became large enough to trigger detection of cancer (labeled as \u0022e\u0022). The cross-sectional path model was derived from sampling all tumors on the basis of their size. Most cross-sectional studies take 1 biopsy per patient and categorize the tumors by size (and\/or grade). To simulate this, we took biopsies of each tumor at prespecified sizes (dashed lines) and then assayed the majority genotype for the biopsy (B). Data from each size class were summarized across all simulations to measure the frequency of mutations for each size class (see \u003Ca id=\u0022xref-fig-4-1\u0022 class=\u0022xref-fig\u0022 href=\u0022#F4\u0022\u003EFig. 4A\u003C\/a\u003E). C, the alternative to cross-sectional sampling is to reconstruct the cell lineages for each tumor. This was done using 5 randomly selected cells from the final time point \u201ce.\u201d During simulations, the cell lineages were recorded, as exact lineage relationships could be derived from detailed genetic data for each cell. The phenotypic effect of each mutation is represented by a different color, defined in \u003Ca id=\u0022xref-fig-3-1\u0022 class=\u0022xref-fig\u0022 href=\u0022#F3\u0022\u003EFigure 3B\u003C\/a\u003E.\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv class=\u0022element-fig-data clearfix figure-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022fig-expansion\u0022 id=\u0022F3\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F3.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022The most common temporal paths found in clones that survived to cancer. A, each of these common temporal paths makes up on an average at least 1% of the neoplasm, and together these 26 paths account for 64% of the cells found in all the cancerous neoplasms. B, each mutation is represented by a different color: loss of differentiation (LD) is green, evasion of apoptosis (EA) is purple, limitless replicative potential (LR) is dark blue, sustained angiogenesis (SA) is red, genomic instability (GI) is light blue, self-sufficiency in growth signals (SG) is yellow, and insensitivity to antigrowth signals (IA) is orange.\u0022 class=\u0022highwire-fragment fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1009679262\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;The most common temporal paths found in clones that survived to cancer. A, each of these common temporal paths makes up on an average at least 1% of the neoplasm, and together these 26 paths account for 64% of the cells found in all the cancerous neoplasms. B, each mutation is represented by a different color: loss of differentiation (LD) is green, evasion of apoptosis (EA) is purple, limitless replicative potential (LR) is dark blue, sustained angiogenesis (SA) is red, genomic instability (GI) is light blue, self-sufficiency in growth signals (SG) is yellow, and insensitivity to antigrowth signals (IA) is orange.\u0026lt;\/div\u0026gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cspan class=\u0022hw-responsive-img\u0022\u003E\u003Cimg class=\u0022highwire-fragment fragment-image lazyload\u0022 alt=\u0022Figure 3.\u0022 src=\u0022data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\u0022 data-src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F3.medium.gif\u0022 width=\u0022440\u0022 height=\u0022373\u0022\/\u003E\u003Cnoscript\u003E\u003Cimg class=\u0022highwire-fragment fragment-image\u0022 alt=\u0022Figure 3.\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F3.medium.gif\u0022 width=\u0022440\u0022 height=\u0022373\u0022\/\u003E\u003C\/noscript\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F3.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 3.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F3.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/28334\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 3.\u003C\/span\u003E \n\u003Cp id=\u0022p-20\u0022\u003EThe most common temporal paths found in clones that survived to cancer. A, each of these common temporal paths makes up on an average at least 1% of the neoplasm, and together these 26 paths account for 64% of the cells found in all the cancerous neoplasms. B, each mutation is represented by a different color: loss of differentiation (LD) is green, evasion of apoptosis (EA) is purple, limitless replicative potential (LR) is dark blue, sustained angiogenesis (SA) is red, genomic instability (GI) is light blue, self-sufficiency in growth signals (SG) is yellow, and insensitivity to antigrowth signals (IA) is orange.\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv class=\u0022element-fig-data clearfix figure-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022fig-expansion\u0022 id=\u0022F4\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F4.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022The temporal order of mutations in cancer clones rarely matches the path order from cross-sectional data, though the temporal order of clones matches the order inferred from the genetic dependency analysis from intratumor data. A, plotting the percentage of tumors with a given mutation at increasing neoplasm sizes can be used to infer (B) the cross-sectional path model of mutations. C, however, the proportion of cells within any given simulated neoplasm whose temporal order is consistent with the cross-sectional path order tends to be low (mean \u0026#xB1; SEM = 7.3% \u0026#xB1; 1.0%, n = 90). D, the proportion of cells within any given simulation whose temporal order is consistent with the inferred order from the genetic dependency analysis is high (mean \u0026#xB1; SEM = 99.7% \u0026#xB1; 0.1%, n = 90). Each mutation is represented by a different color as given in Figure 3B.\u0022 class=\u0022highwire-fragment fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1009679262\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;The temporal order of mutations in cancer clones rarely matches the path order from cross-sectional data, though the temporal order of clones matches the order inferred from the genetic dependency analysis from intratumor data. A, plotting the percentage of tumors with a given mutation at increasing neoplasm sizes can be used to infer (B) the cross-sectional path model of mutations. C, however, the proportion of cells within any given simulated neoplasm whose temporal order is consistent with the cross-sectional path order tends to be low (mean \u0026#xB1; SEM = 7.3% \u0026#xB1; 1.0%, n = 90). D, the proportion of cells within any given simulation whose temporal order is consistent with the inferred order from the genetic dependency analysis is high (mean \u0026#xB1; SEM = 99.7% \u0026#xB1; 0.1%, n = 90). Each mutation is represented by a different color as given in Figure 3B.\u0026lt;\/div\u0026gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cspan class=\u0022hw-responsive-img\u0022\u003E\u003Cimg class=\u0022highwire-fragment fragment-image lazyload\u0022 alt=\u0022Figure 4.\u0022 src=\u0022data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\u0022 data-src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F4.medium.gif\u0022 width=\u0022440\u0022 height=\u0022306\u0022\/\u003E\u003Cnoscript\u003E\u003Cimg class=\u0022highwire-fragment fragment-image\u0022 alt=\u0022Figure 4.\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F4.medium.gif\u0022 width=\u0022440\u0022 height=\u0022306\u0022\/\u003E\u003C\/noscript\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F4.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 4.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F4.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/28397\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 4.\u003C\/span\u003E \n\u003Cp id=\u0022p-22\u0022\u003EThe temporal order of mutations in cancer clones rarely matches the path order from cross-sectional data, though the temporal order of clones matches the order inferred from the genetic dependency analysis from intratumor data. A, plotting the percentage of tumors with a given mutation at increasing neoplasm sizes can be used to infer (B) the cross-sectional path model of mutations. C, however, the proportion of cells within any given simulated neoplasm whose temporal order is consistent with the cross-sectional path order tends to be low (mean \u00b1 SEM = 7.3% \u00b1 1.0%, \u003Cem\u003En\u003C\/em\u003E = 90). D, the proportion of cells within any given simulation whose temporal order is consistent with the inferred order from the genetic dependency analysis is high (mean \u00b1 SEM = 99.7% \u00b1 0.1%, \u003Cem\u003En\u003C\/em\u003E = 90). Each mutation is represented by a different color as given in \u003Ca id=\u0022xref-fig-3-3\u0022 class=\u0022xref-fig\u0022 href=\u0022#F3\u0022\u003EFigure 3B\u003C\/a\u003E.\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003Cli class=\u0022last\u0022\u003E\u003Cdiv class=\u0022element-fig-data clearfix figure-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022fig-expansion\u0022 id=\u0022F5\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022highwire-figure\u0022\u003E\u003Cdiv class=\u0022fig-inline-img-wrapper\u0022\u003E\u003Cdiv class=\u0022fig-inline-img\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F5.large.jpg?width=800\u0026amp;height=600\u0026amp;carousel=1\u0022 title=\u0022Details of a single simulation as it progresses to cancer. A, plot of the percentage of cells within this neoplasm that contain a given mutation over time. Note that the IA reaches detection early in progression and regresses. B, plot of the Shannon index for diversity, or information entropy, over time for the simulation. C, top, the clones, their mutational states, and their rough population sizes over time. The height is proportional to the population size of the neoplasm, and new mutations are indicated with an arrow. Bottom, the type of neoplasm that would be identified at various points during progression from normal tissue to cancer, beginning with polyps and ending with cancer. D, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm shows that a single evolutionary run does not have a single evolutionary path. The temporal order of phenotypes is given at the tips of the genealogy. Because we are modeling phenotypes, the same set of phenotypic mutations can occur in clones that are unique by descent. Each new mutation for a phenotype is a new mutation in a gene or pathway conferring the phenotype. Thus, we have what looks like convergent evolution\u0026#x2014;there is phenotypic homogeneity but it arose through different genetic alterations. Under these parameters, independent acquisition of hallmarks in different clones is common and leads to clonal interference and the suppression of clonal expansion for any one clone. Note that the most commonly observed phenotypic order does not correspond to the cross-sectional path order given in Figure 3B. E, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm pictured in Figure 2. Both neoplasms pictured here have relatively high genetic heterogeneity at cancer detection. As occurs here, genetic heterogeneity may lead to phenotypic homogeneity. Each mutation is represented by a different color as given in Figure 3B.\u0022 class=\u0022highwire-fragment fragment-images colorbox-load\u0022 rel=\u0022gallery-fragment-images-1009679262\u0022 data-figure-caption=\u0022\u0026lt;div class=\u0026quot;highwire-markup\u0026quot;\u0026gt;Details of a single simulation as it progresses to cancer. A, plot of the percentage of cells within this neoplasm that contain a given mutation over time. Note that the IA reaches detection early in progression and regresses. B, plot of the Shannon index for diversity, or information entropy, over time for the simulation. C, top, the clones, their mutational states, and their rough population sizes over time. The height is proportional to the population size of the neoplasm, and new mutations are indicated with an arrow. Bottom, the type of neoplasm that would be identified at various points during progression from normal tissue to cancer, beginning with polyps and ending with cancer. D, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm shows that a single evolutionary run does not have a single evolutionary path. The temporal order of phenotypes is given at the tips of the genealogy. Because we are modeling phenotypes, the same set of phenotypic mutations can occur in clones that are unique by descent. Each new mutation for a phenotype is a new mutation in a gene or pathway conferring the phenotype. Thus, we have what looks like convergent evolution\u0026#x2014;there is phenotypic homogeneity but it arose through different genetic alterations. Under these parameters, independent acquisition of hallmarks in different clones is common and leads to clonal interference and the suppression of clonal expansion for any one clone. Note that the most commonly observed phenotypic order does not correspond to the cross-sectional path order given in Figure 3B. E, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm pictured in Figure 2. Both neoplasms pictured here have relatively high genetic heterogeneity at cancer detection. As occurs here, genetic heterogeneity may lead to phenotypic homogeneity. Each mutation is represented by a different color as given in Figure 3B.\u0026lt;\/div\u0026gt;\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cspan class=\u0022hw-responsive-img\u0022\u003E\u003Cimg class=\u0022highwire-fragment fragment-image lazyload\u0022 alt=\u0022Figure 5.\u0022 src=\u0022data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\u0022 data-src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F5.medium.gif\u0022 width=\u0022422\u0022 height=\u0022440\u0022\/\u003E\u003Cnoscript\u003E\u003Cimg class=\u0022highwire-fragment fragment-image\u0022 alt=\u0022Figure 5.\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F5.medium.gif\u0022 width=\u0022422\u0022 height=\u0022440\u0022\/\u003E\u003C\/noscript\u003E\u003C\/span\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cul class=\u0022highwire-figure-links inline\u0022\u003E\u003Cli class=\u0022download-fig first\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F5.large.jpg?download=true\u0022 class=\u0022highwire-figure-link highwire-figure-link-download\u0022 title=\u0022Download Figure 5.\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload figure\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022new-tab\u0022\u003E\u003Ca href=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/content\/canprevres\/4\/7\/1135\/F5.large.jpg\u0022 class=\u0022highwire-figure-link highwire-figure-link-newtab\u0022 target=\u0022_blank\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EOpen in new tab\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022download-ppt last\u0022\u003E\u003Ca href=\u0022\/highwire\/powerpoint\/28364\u0022 class=\u0022highwire-figure-link highwire-figure-link-ppt\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EDownload powerpoint\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003Cdiv class=\u0022fig-caption\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cspan class=\u0022fig-label\u0022\u003EFigure 5.\u003C\/span\u003E \n\u003Cp id=\u0022p-28\u0022\u003EDetails of a single simulation as it progresses to cancer. A, plot of the percentage of cells within this neoplasm that contain a given mutation over time. Note that the IA reaches detection early in progression and regresses. B, plot of the Shannon index for diversity, or information entropy, over time for the simulation. C, top, the clones, their mutational states, and their rough population sizes over time. The height is proportional to the population size of the neoplasm, and new mutations are indicated with an arrow. Bottom, the type of neoplasm that would be identified at various points during progression from normal tissue to cancer, beginning with polyps and ending with cancer. D, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm shows that a single evolutionary run does not have a single evolutionary path. The temporal order of phenotypes is given at the tips of the genealogy. Because we are modeling phenotypes, the same set of phenotypic mutations can occur in clones that are unique by descent. Each new mutation for a phenotype is a new mutation in a gene or pathway conferring the phenotype. Thus, we have what looks like convergent evolution\u2014there is phenotypic homogeneity but it arose through different genetic alterations. Under these parameters, independent acquisition of hallmarks in different clones is common and leads to clonal interference and the suppression of clonal expansion for any one clone. Note that the most commonly observed phenotypic order does not correspond to the cross-sectional path order given in \u003Ca id=\u0022xref-fig-3-4\u0022 class=\u0022xref-fig\u0022 href=\u0022#F3\u0022\u003EFigure 3B\u003C\/a\u003E. E, the genealogy, or cell lineage, for all of the clones that arose during the evolution of the neoplasm pictured in \u003Ca id=\u0022xref-fig-2-4\u0022 class=\u0022xref-fig\u0022 href=\u0022#F2\u0022\u003EFigure 2\u003C\/a\u003E. Both neoplasms pictured here have relatively high genetic heterogeneity at cancer detection. As occurs here, genetic heterogeneity may lead to phenotypic homogeneity. Each mutation is represented by a different color as given in \u003Ca id=\u0022xref-fig-3-5\u0022 class=\u0022xref-fig\u0022 href=\u0022#F3\u0022\u003EFigure 3B\u003C\/a\u003E.\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022fig-data-tables\u0022 class=\u0022group frag-table\u0022\u003E\u003Cdiv class=\u0022fig-data-title-jump clearfix\u0022\u003E\u003Ch3 class=\u0022fig-data-group-title\u0022\u003ETables\u003C\/h3\u003E\u003Cdiv class=\u0022fig-data-jump-links\u0022\u003E\u003Cul class=\u0022fig-data-jump-links-list links inline\u0022\u003E\u003Cli class=\u0022figure first\u0022\u003E\u003Ca href=\u0022#fig-data-figures\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-figure link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-up\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003EFigures\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022additional-files last\u0022\u003E\u003Ca href=\u0022#fig-data-additional-files\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-additional-files link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-down\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003EAdditional Files\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022item-list\u0022\u003E\u003Cul class=\u0022fig-data-list clearfix\u0022 id=\u0022fragments-table\u0022\u003E\u003Cli class=\u0022first last\u0022\u003E\u003Cdiv class=\u0022element-fig-data clearfix table-caption\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022table-expansion\u0022 id=\u0022T1\u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022table-caption\u0022\u003E\u003Cspan class=\u0022table-label\u0022\u003ETable 1.\u003C\/span\u003E \n\u003Cp id=\u0022p-10\u0022\u003EHallmarks of cancer phenotypes and their effects on the model when mutated\u003C\/p\u003E\n\u003Cdiv class=\u0022sb-div caption-clear\u0022\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Ctable id=\u0022table-1\u0022\u003E\u003Cthead id=\u0022thead-1\u0022\u003E\u003Ctr id=\u0022tr-1\u0022\u003E\u003Cth align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022th-1\u0022 class=\u0022table-left\u0022\u003EPhenotype\u003C\/th\u003E\u003Cth align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022th-2\u0022 class=\u0022table-left\u0022\u003EModel effect\u003C\/th\u003E\u003C\/tr\u003E\u003C\/thead\u003E\u003Ctbody id=\u0022tbody-1\u0022\u003E\u003Ctr id=\u0022tr-2\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-1\u0022 class=\u0022table-left\u0022\u003EInsensitivity to antigrowth signals\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-2\u0022 class=\u0022table-left\u0022\u003ECells with this mutation have an increased probability for cell division\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-3\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-3\u0022 class=\u0022table-left\u0022\u003ESelf-sufficiency in growth signals\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-4\u0022 class=\u0022table-left\u0022\u003ECells with this mutation have an increased probability for cell division\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-4\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-5\u0022 class=\u0022table-left\u0022\u003EEvasion of apoptosis\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-6\u0022 class=\u0022table-left\u0022\u003ECells with this mutation have a decreased probability of apoptosis\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-5\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-7\u0022 class=\u0022table-left\u0022\u003ELimitless replicative potential\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-8\u0022 class=\u0022table-left\u0022\u003EMutation eliminates telomere loss during cell division. Cells with this mutation can divide more times than those without.\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-6\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-9\u0022 class=\u0022table-left\u0022\u003ESustained angiogenesis\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-10\u0022 class=\u0022table-left\u0022\u003ECells with this mutation increase the number of cells that can survive in a tumor.\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-7\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-11\u0022 class=\u0022table-left\u0022\u003ELoss of differentiation\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-12\u0022 class=\u0022table-left\u0022\u003ECells with this mutation no longer differentiate at cell division.\u003C\/td\u003E\u003C\/tr\u003E\u003Ctr id=\u0022tr-8\u0022\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-13\u0022 class=\u0022table-left\u0022\u003EGenome instability\u003C\/td\u003E\u003Ctd align=\u0022left\u0022 rowspan=\u00221\u0022 colspan=\u00221\u0022 id=\u0022td-14\u0022 class=\u0022table-left\u0022\u003ECells with this mutation have increased chances to acquire a new mutation.\u003C\/td\u003E\u003C\/tr\u003E\u003C\/tbody\u003E\u003C\/table\u003E\u003Cdiv class=\u0022table-foot\u0022\u003E\u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv id=\u0022fig-data-additional-files\u0022 class=\u0022group frag-additional-files\u0022\u003E\u003Cdiv class=\u0022fig-data-title-jump clearfix\u0022\u003E\u003Ch3 class=\u0022fig-data-group-title\u0022\u003EAdditional Files\u003C\/h3\u003E\u003Cdiv class=\u0022fig-data-jump-links\u0022\u003E\u003Cul class=\u0022fig-data-jump-links-list links inline\u0022\u003E\u003Cli class=\u0022figure first\u0022\u003E\u003Ca href=\u0022#fig-data-figures\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-figure link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-up\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003EFigures\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003Cli class=\u0022table last\u0022\u003E\u003Ca href=\u0022#fig-data-tables\u0022 class=\u0022fig-data-jump-link fig-data-jump-link-table link-icon\u0022\u003E\u003Ci class=\u0022icon-caret-up\u0022\u003E\u003C\/i\u003E \u003Cspan class=\u0022title\u0022\u003ETables\u003C\/span\u003E\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv class=\u0022item-list\u0022\u003E\u003Cul class=\u0022fig-data-list clearfix\u0022 id=\u0022fragments-additional-data\u0022\u003E\u003Cli class=\u0022first last\u0022\u003E\u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022auto-clean\u0022\u003E\u003Cspan style=\u0022font-family: Verdana,Arial,Helvetica,sans-serif; font-size: 83.33%\u0022\u003E\n \n \u003Ch2\u003ESupplementary Data\u003C\/h2\u003E\n \u003Cp\u003E\u003Cstrong\u003EFiles in this Data Supplement:\u003C\/strong\u003E\u003C\/p\u003E\n \u003Cul\u003E\u003Cli\u003E\u003Ca href=\u0022\/highwire\/filestream\/35991\/field_highwire_adjunct_files\/0\/0374_supp_mats.pdf\u0022 class=\u0022rewritten\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003ESupplementary Materials\u003C\/a\u003E \n \t\t\n \u003C\/li\u003E\u003C\/ul\u003E\n \u003C\/span\u003E\n \n \u003C\/div\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E \u003C\/div\u003E\n\n \n \u003C\/div\u003E\n\u003Cdiv class=\u0022panel-separator\u0022\u003E\u003C\/div\u003E\u003Cdiv class=\u0022panel-pane pane-earthchem\u0022 \u003E\n \n \n \n \u003Cdiv class=\u0022pane-content\u0022\u003E\n \u003Ca href=\u0022http:\/\/ecp.iedadata.org\/doidata\/10.1158\/1940-6207.CAPR-10-0374\u0022 class=\u0022\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003E\u003Cimg src=\u0022http:\/\/ecp.iedadata.org\/doibanner\/10.1158\/1940-6207.CAPR-10-0374\u0022 alt=\u0022\u0022 \/\u003E\u003C\/a\u003E \u003C\/div\u003E\n\n \n \u003C\/div\u003E\n\u003C\/div\u003E\n \u003C\/div\u003E\n\u003C\/div\u003E\n\u003C\/div\u003E\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/cancerpreventionresearch.aacrjournals.org\/sites\/default\/files\/js\/js_hZg96SP9gBcOluDp2mGc57d8sP8uJ7g8P_JYsCISOgQ.js\u0022\u003E\u003C\/script\u003E\n\u003C\/body\u003E\u003C\/html\u003E"}